With the development of vehicle technology, automatic driving of vehicles has become a hot research field. Speed planning and control is an important research subject in automatic driving, the primary goal of which is to plan an estimated speed of a vehicle at a series of subsequent time instants according to a detected state (e.g., a current speed, a speed of a front vehicle, and a distance from a front vehicle), and to calculate final control parameters (e.g., an accelerator and a break) of the vehicle to actually control the vehicle. Speed planning needs to ensure absolute safety of passengers when other vehicles have unexpected behaviors (e.g., sudden braking) while ensuring the basic comfort and safety of the passengers.
To deal with various possible situations, it needs to design a complex speed planning and control model. Some rare circumstances and factors are very likely to be left out in manual design and implementation. At the same time, very rich and mature driving data can be acquired from a vehicle driven by a driver. Since a machine learning method can easily learn a model from the data, the machine learning method is increasingly applied to the speed planning and control. In addition, in the real world, different drivers have different driving habits and definitions of safety and comfort. Therefore, if the same planning and control method is used, it is difficult to meet various different needs, while the machine learning method can well adapt to personalized driving habits.